International Journal of Advanced Technology and Engineering Exploration (IJATEE) ISSN (P): 2394-5443 ISSN (O): 2394-7454 Vol - 10, Issue - 108, November 2023
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Secure distance based multi-objective artificial rabbits algorithm for clustering and routing in cognitive radio network

K. N. Shyleshchandra Gudihatti and K. Pradeep Kumar

Abstract

Cognitive radio networks (CRNs) are presently undergoing extensive research and are gaining popularity in a wide range of applications. The nodes in a cognitive radio sensor network have flexibility according to data packets due to dynamic transmission techniques. In this study, the energy consumption within the clustering/routing is taken into account for determining the ideal transmission distance. The cluster size is adjusted depending on the number of packets in the cluster and how the nodes are grouped in a clustered shape. Additionally, due to the cognitive capabilities of the sensor node, it is possible to determine the remaining duration of licensed channels that are not in use in a CRN. Secure distance multi-objective artificial rabbits’ algorithm (SD-MOARA) based clustering and routing is utilized to fulfill the extendable efficiency in CRN. The goal of the suggested routing system is to forward data packets along lines that utilize the least amount of energy. The outcomes of the proposed SD-MOARA are examined using MATLAB in terms of the following performances: remaining energy (851.4 J), packet delivery ratio (99.9%), packet loss rate (PLR) (0.2%), energy consumption (23.9 J), throughput (0.99 Mbps), average delay (0.42 s) and routing overhead (0.40). The above-stated results demonstrate that the proposed SD-MOARA outperforms the conventional methods.

Keyword

Cognitive radio networks, Clustering, Distance, Multi-objective artificial rabbits algorithm, Routing.

Cite this article

Shyleshchandra Gudihatti KN, Kumar KP

Refference

[1][1]Joon R, Tomar P. Energy aware Q-learning AODV (EAQ-AODV) routing for cognitive radio sensor networks. Journal of King Saud University-Computer and Information Sciences. 2022; 34(9):6989-7000.

[2][2]Freitas PV, Hanthequeste RF, Orofino G, Castellanos PV, Canavitsas ÂA, Bentes RC. Implementation of a spectrum analyzer using the software-defined radio concept. Journal of Microwaves, Optoelectronics and Electromagnetic Applications. 2021; 20:801-11.

[3][3]Monisha M, Rajendran V. SCAN-CogRSG: secure channel allocation by dynamic cluster switching for cognitive radio enabled smart grid communications. IETE Journal of Research. 2022; 68(4):2826-47.

[4][4]Paul A, Maity SP. Machine learning for spectrum information and routing in multihop green cognitive radio networks. IEEE Transactions on Green Communications and Networking. 2021; 6(2):825-35.

[5][5]Tran TN, Nguyen TV, Shim K, Da CDB, An B. A new deep Q-network design for QoS multicast routing in cognitive radio MANETs. IEEE Access. 2021; 9:152841-56.

[6][6]Wang J, Li C. A weighted energy consumption minimization-based multi-hop uneven clustering routing protocol for cognitive radio sensor networks. Scientific Reports. 2022; 12(1):1-16.

[7][7]Kumar S, Singh AK. A localized algorithm for clustering in cognitive radio networks. Journal of King Saud University-Computer and Information Sciences. 2021; 33(5):600-7.

[8][8]Bhatt R, Onyema EM, Almuzaini KK, Iwendi C, Band SS, Sharma T, et al. Assessment of dynamic swarm heterogeneous clustering in cognitive radio sensor networks. Wireless Communications and Mobile Computing. 2022; 2022:1-15.

[9][9]Stephan T, Al-turjman F, Joseph KS, Balusamy B, Srivastava S. Artificial intelligence inspired energy and spectrum aware cluster based routing protocol for cognitive radio sensor networks. Journal of Parallel and Distributed Computing. 2020; 142:90-105.

[10][10]Paul A, Choi K. Joint spectrum sensing and D2D communications in cognitive radio networks using clustering and deep learning strategies under SSDF attacks. Ad Hoc Networks. 2023; 143:103116.

[11][11]Srivastava V, Tripathi S, Singh K, Son LH. Energy efficient optimized rate based congestion control routing in wireless sensor network. Journal of Ambient Intelligence and Humanized Computing. 2020; 11:1325-38.

[12][12]Cicioğlu M, Çalhan A, Miah MS. An effective routing algorithm for spectrum allocations in cognitive radio based internet of things. Concurrency and Computation: Practice and Experience. 2022; 34(28):e7368.

[13][13]Wang J, Li S. ECE: a novel performance evaluation metric for clustering protocols in cognitive radio sensor networks. IEEE Internet of Things Journal. 2020; 8(3):2078-9.

[14][14]Kalra M, Vohra A, Marriwala N. LEACH based hybrid energy efficient routing algorithm for dynamic cognitive radio networks. Measurement: Sensors. 2022; 24:100419.

[15][15]Aravindkumaran S, Saraswady D, Sedhumadhavan S. Transmission power control based on cross layer routing optimization technique in cognitive radio network. International Journal of Intelligent Systems and Applications in Engineering. 2023; 11(9s):511-27.

[16][16]Naveen RR, Nayak A, Kumar MS. A survey and performance evaluation of reinforcement learning based spectrum aware routing in cognitive radio ad hoc networks. International Journal of Wireless Information Networks. 2020; 27(1):144-63.

[17][17]Buzura S, Iancu B, Dadarlat V, Peculea A, Cebuc E. Optimizations for energy efficiency in software-defined wireless sensor networks. Sensors. 2020; 20(17):1-23.

[18][18]Samarji N, Salamah M. ESRA: energy soaring-based routing algorithm for IoT applications in software-defined wireless sensor networks. Egyptian Informatics Journal. 2022; 23(2):215-24.

[19][19]Bakr R, Aziz EAA, El-shaikh SA, Tag ELAS. Energy efficient spectrum aware distributed clustering in cognitive radio sensor networks. In proceedings of the international conference on advanced intelligent systems and informatics 2020 (pp. 517-26). Springer International Publishing.

[20][20]Mortada MR, Nasser A, Mansour A, Yao KC. In-network data aggregation for ad hoc clustered cognitive radio wireless sensor network. Sensors. 2021; 21(20):1-25.

[21][21]Zheng M, Wang C, Song M, Liang W, Yu H. SACR: a stability-aware cluster-based routing protocol for cognitive radio sensor networks. IEEE Sensors Journal. 2021; 21(15):17350-9.

[22][22]Ramkumar J, Vadivel R. Improved Wolf prey inspired protocol for routing in cognitive radio ad hoc networks. International Journal of Computer Networks and Applications. 2020; 7(5):126-36.

[23][23]Tripathi Y, Prakash A, Tripathi R. An optimum transmission distance and adaptive clustering based routing protocol for cognitive radio sensor network. Wireless Personal Communications. 2021; 116:907-26.

[24][24]Jyothi V, Subramanyam MV. An enhanced routing technique to improve the network lifetime of cognitive sensor network. Wireless Personal Communications. 2022; 127(2):1241-64.

[25][25]Ramkumar J, Vadivel R. Whale optimization routing protocol for minimizing energy consumption in cognitive radio wireless sensor network. International Journal of Computer Networks and Applications. 2021; 8(1):455-64.

[26][26]Vivekanand CV, Inbamalar TM, Nadar KP, Kannagi V, Arthi DP. Energy-efficient compressed sensing in cognitive radio network for telemedicine services. Wireless Communications and Mobile Computing. 2023; 2023:1-12.

[27][27]Srividhya V, Shankar T. An energy efficient distance-based spectrum aware hybrid optimization technique for cognitive radio wireless sensor network. Journal of the Institution of Engineers (India): Series B. 2023; 104(1):51-60.

[28][28]Gupta A, Joshi BK. Efficient optimized ATSDERP routing based DEQRL spectrum sharing HPNCS network coding model in cognitive radio networks. Wireless Personal Communications. 2023; 129(4):2995-3022.

[29][29]Darabkh KA, Awawdeh BR, Saifan RR, Khalifeh AF, Alnabelsi SH, Bany SH. Routing in cognitive radio networks using adaptive full-duplex communications over IoT environment. Wireless Networks. 2023; 29(3):1439-63.

[30][30]Salih QM, Rahman MA, Asyhari AT, Naeem MK, Patwary M, Alturki R, et al. Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications. Digital Communications and Networks. 2023; 9(2):367-82.

[31][31]Wang J, Ge Y. A radio frequency energy harvesting-based multihop clustering routing protocol for cognitive radio sensor networks. IEEE Sensors Journal. 2022; 22(7):7142-56.

[32][32]Rai P, Ghose MK, Sarma HK. Game theory based node clustering for cognitive radio wireless sensor networks. Egyptian Informatics Journal. 2022; 23(2):315-27.

[33][33]Arat F, Demirci S. Channel switching cost-aware energy efficient routing in cognitive radio-enabled internet of things. Mobile Networks and Applications. 2022; 27(4):1531-50.

[34][34]Safdar GA, Syed TS, Ur-rehman M. Fuzzy logic-based cluster head election-led energy efficiency in history-assisted cognitive radio networks. IEEE Sensors Journal. 2022; 22(22):22117-26.

[35][35]Jyothi V, Subramanyam MV. An energy efficient fuzzy clustering-based congestion control algorithm for cognitive radio sensor networks. Wireless Networks. 2022:1-6.

[36][36]Sunitha D, Balmuri KR, De PRP, Divakarachari PB, Vijayarangan R, Hemalatha KL. Congestion centric multi‐objective reptile search algorithm‐based clustering and routing in cognitive radio sensor network. Transactions on Emerging Telecommunications Technologies. 2022:e4629.

[37][37]Wang L, Cao Q, Zhang Z, Mirjalili S, Zhao W. Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence. 2022; 114:105082.

[38][38]Vellingiri M, Rawa M, Alghamdi S, Alhussainy AA, Ali ZM, Turky RA, et al. Maximum hosting capacity estimation for renewables in power grids considering energy storage and transmission lines expansion using hybrid sine cosine artificial rabbits algorithm. Ain Shams Engineering Journal. 2023; 14(5):102092.

[39][39]Gülmez B. Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Systems with Applications. 2023; 227:120346.

[40][40]Khalil AE, Boghdady TA, Alham MH, Ibrahim DK. Enhancing the conventional controllers for load frequency control of isolated microgrids using proposed multi-objective formulation via artificial rabbits optimization algorithm. IEEE Access. 2023; 11:3472-93.

[41][41]Kumar DS, Premkumar M, Kumar C, Muyeen SM. Optimal scheduling algorithm for residential building distributed energy source systems using levy flight and chaos-assisted artificial rabbits optimizer. Energy Reports. 2023; 9:5721-40.

[42][42]Riad AJ, Hasanien HM, Turky RA, Yakout AH. Identifying the PEM fuel cell parameters using artificial rabbits optimization algorithm. Sustainability. 2023; 15(5):1-17.

[43][43]Awadallah MA, Braik MS, Al-betar MA, Abu DI. An enhanced binary artificial rabbits optimization for feature selection in medical diagnosis. Neural Computing and Applications. 2023; 35(27):20013-68.

[44][44]Viyyapu LV, Godavarthi S, Rani SB, Gurrala VR, Gurrala VS. Effective route failure recovery mechanism on multicast routing in cognitive radio networks. International Journal of Intelligent Engineering & Systems. 2022; 15(3):71-9.

[45][45]Sugitha G, Sivakumar TB, Hasan HSH. QoS aware routing protocol using robust spatial gabriel graph based clustering scheme for ad hoc network. Concurrency and Computation: Practice and Experience. 2022; 34(27):e7309.